Triple
T22277484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ratlam Junction railway station |
E550644
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
RTM
RTM is the station code for Ratlam Junction, a major railway junction in the Indian state of Madhya Pradesh.
|
E1528991
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: RTM | Statement: [Ratlam Junction railway station, stationCode, RTM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RTM Context triple: [Ratlam Junction railway station, stationCode, RTM]
-
A.
RTM
RTM is the commonly used abbreviation for Rosetta Terminology Mapping, a system for standardizing and aligning terminology across different datasets or domains.
-
B.
RTM
RTM is the IATA airport code for Rotterdam The Hague Airport, a regional international airport serving the Rotterdam–The Hague metropolitan area in the Netherlands.
-
C.
RTM
RTM is the public transport authority that operates buses, trams, and metro services in Marseille and its surrounding metropolitan area in France.
-
D.
RTMG
RTMG is a specialized working group focused on analyzing emerging risks, trends, and methodological issues in its domain.
-
E.
RMT
RMT is the commonly used abbreviation for the United Nations Conference on Trade and Development’s annual Review of Maritime Transport report, which analyzes global maritime trade and shipping trends.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: RTM Triple: [Ratlam Junction railway station, stationCode, RTM]
Generated description
RTM is the station code for Ratlam Junction, a major railway junction in the Indian state of Madhya Pradesh.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RTM Target entity description: RTM is the station code for Ratlam Junction, a major railway junction in the Indian state of Madhya Pradesh.
-
A.
RTM
RTM is the commonly used abbreviation for Rosetta Terminology Mapping, a system for standardizing and aligning terminology across different datasets or domains.
-
B.
RTM
RTM is the IATA airport code for Rotterdam The Hague Airport, a regional international airport serving the Rotterdam–The Hague metropolitan area in the Netherlands.
-
C.
RTM
RTM is the public transport authority that operates buses, trams, and metro services in Marseille and its surrounding metropolitan area in France.
-
D.
RTMG
RTMG is a specialized working group focused on analyzing emerging risks, trends, and methodological issues in its domain.
-
E.
RMT
RMT is the commonly used abbreviation for the United Nations Conference on Trade and Development’s annual Review of Maritime Transport report, which analyzes global maritime trade and shipping trends.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f14ea8a6688190bcd168670420ec59 |
completed | April 29, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0abca612008190bcb1c38523223ed7 |
completed | May 18, 2026, 7:15 a.m. |
| NEDg | Description generation | batch_6a0ac0d258d88190a73427e02373a4bc |
completed | May 18, 2026, 7:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ac1e9666c8190b183c069deaf487c |
completed | May 18, 2026, 7:38 a.m. |
Created at: April 16, 2026, 8:40 p.m.